Impact of data subsamplings in Fast Multi-Scale Neighbor Embedding

Fast multi-scale neighbor embedding (f-ms-NE) is an algorithm that maps high-dimensional data to a low-dimensional space by preserving the multi-scale data neighborhoods. To lower its time complexity, f-ms-NE uses random subsamplings to estimate the data properties at multiple scales. To improve this estimation and study the f-ms-NE sensitivity to randomness, this paper generalizes the f-ms-NE cost function by averaging several subsamplings. Experiments reveal that this can slightly improve the quality of the embeddings while maintaining reasonable computation times. Codes are available at "https://github.com/cdebodt/Fast_Multi-scale_NE"